This adds a new quantization mechanism for HNSW and flat indices. Here
we add `int4` quantization via the `int4_hnsw` and `int4_flat` index
types. This quantization methodology further reduces the memory required
for fast HNSW, meaning that the memory required is 8x smaller than with
regular float32 values.
8x reduction means that 1M 1024 dimension vectors goes from requiring
3.8GB to 477MB.
Recall continues to stay steady, there is some reduction that is
recoverable via slightly oversampling and reranking. For example over
500k CohereV3 vectors, only 5 extra vectors are required to be gathered
to achieve over 0.98 recall in a brute-force scenario.
* Initial docs for put-inference for Mistral
* adds mistral embeddings to tutorial; add changelog
* update mistral text and dimensions
* fix mistral spelling error
* fix azure AI studio; fix Mistral label
* fix auto-formatted items
* change pipeline button back to azure openai
* put proper Azure AI Studio include in
* fix missing azure-openai; fix huggingface hidden
* fix mistral tab for reindex
* re-add Mistral service settings to put inference
* Update Search Applications docs with more introductory information about search templates
* Add docs tests
* Skip test
* Fix test
* Unskip test
* Add comment RE: reasoning behind test setup
Add support to the Unified highlighter to combine matches on multiple fields
to highlight a single field: "matched_fields".
Based on Lucene PR: https://github.com/apache/lucene/pull/13268
Lucene PR is based on the concept of masked fields where masked fields
are different from the original highlighted field. This PR in
Elasticsearch uses the already existing highlighter parameter
"matched_fields".
In order to improve the experience of cross-cluster search, we are changing
the default value of the remote cluster `skip_unavailable` setting from `false` to `true`.
This setting causes any cross-cluster _search (or _async_search) to entirely fail when
any remote cluster with `skip_unavailable=false` is either unavailable (connection to it fails)
or if the search on it fails on all shards.
Setting `skip_unavailable=true` allows partial results from other clusters to be
returned. In that case, the search response cluster metadata will show a `skipped`
status, so the user can see that no data came in from that cluster. Kibana also
now leverages this metadata in the cross-cluster search responses to allow users
to see how many clusters returned data and drill down into which clusters did not
(including failure messages).
Currently, the user/admin has to specifically set the value to `true` in the configs, like so:
```
cluster:
remote:
remote1:
seeds: 10.10.10.10:9300
skip_unavailable: true
```
even though that is probably what search admins want in the vast majority of cases.
Setting `skip_unavailable=false` should be a conscious (and probably rare) choice
by an Elasticsearch admin that a particular cluster's results are so essential to a
search (or visualization in dashboard or Discover panel) that no results at all should
be shown if it cannot return any results.
During the fetch phase, there's a number of stored fields that are requested explicitly or loaded by default. That information is included in `StoredFieldsSpec` that each fetch sub phase exposes.
We attempt to provide stored fields that are already loaded to the fields lookup that scripts as well as value fetchers use to load field values (via `SearchLookup`). This is done in `PreloadedFieldLookupProvider.` The current logic makes available values for fields that have been found, so that scripts or value fetchers that request them don't load them again ad-hoc. What happens though for stored fields that don't have a value for a specific doc, is that they are treated like any other field that was not requested, and loaded again, although they will not be found, which causes overhead.
This change makes available to `PreloadedFieldLookupProvider` the list of required stored fields, so that it can better distinguish between fields that we already attempted to load (although we may not have found a value for them) and those that need to be loaded ad-hoc (for instance because a script is requesting them for the first time).
This is an existing issue, that has become evident as we moved fetching of metadata fields to `FetchFieldsPhase`, that relies on value fetchers, and hence on `SearchLookup`. We end up attempting to load default metadata fields (`_ignored` and `_routing`) twice when they are not present in a document, which makes us call `LeafReader#storedFields` additional times for the same document providing a `SingleFieldVisitor` that will never find a value.
Another existing issue that this PR fixes is for the `FetchFieldsPhase` to extend the `StoredFieldsSpec` that it exposes to include the metadata fields that the phase is now responsible for loading. That results in `_ignored` being included in the output of the debug stored fields section when profiling is enabled. The fact that it was previously missing is an existing bug (it was missing in `StoredFieldLoader#fieldsToLoad`).
Yet another existing issues that this PR fixes is that `_id` has been until now always loaded on demand when requested via fetch fields or script. That is because it is not part of the preloaded stored fields that the fetch phase passes over to the `PreloadedFieldLookupProvider`. That causes overhead as the field has already been loaded, and should not be loaded once again when explicitly requested.
* Remove `es-test-dir` book-scoped variable
* Remove `plugins-examples-dir` book-scoped variable
* Remove `:dependencies-dir:` and `:xes-repo-dir:` book-scoped variables
- In `index.asciidoc`, two variables (`:dependencies-dir:` and `:xes-repo-dir:`) were removed.
- In `sql/index.asciidoc`, the `:sql-tests:` path was updated to fuller path
- In `esql/index.asciidoc`, the `:esql-tests:` path was updated idem
* Replace `es-repo-dir` with `es-ref-dir`
* Move `:include-xpack: true` to few files that use it, remove from index.asciidoc
Here we extract the logic to populate metadata fields such as _ignored, _routing, _size and the deprecated _type into FetchFieldsPhase so that we can use the ValueFetcher interface to retrieve field values. This allows us to fetch values no matter if the Mapper uses stored or doc values.
For float32, there is no compelling reason to use all the memory
required by default for HNSW. Using `int8_hnsw` provides a much saner
default when it comes to cost vs relevancy.
So, on all new indices that use `dense_vector` and want to index them
for fast search, we will default to `int8_hnsw`.
Users can still customize their parameters, or prefer `hnsw` over
float32 if they so desire.
The typed_keys request parameter is the canonical parameter,
that's also used in the regular index _search enpoint, in order to
return the types of aggregations in the response.
This is required by typed language clients of the _security/_query/api_key
endpoint that are using aggregations.
Closes#106817
Kathleen DeRusso
George Wallace
István Zoltán Szabó
Pius
Benjamin Trent
Benjamin Trent
Panagiotis Bailis
Mark J. Hoy
elasticsearchmachine
Liam Thompson
ChrisHegarty
Mayya Sharipova
Michael Peterson
eyalkoren
Jim Ferenczi
Luca Cavanna
Salvatore Campagna
Bogdan Pintea
Albert Zaharovits
Jack Conradson
Ioana Tagirta
Aurélien FOUCRET